Frontiers in Systems Neuroscience最新文献

Objective: To identify common functional brain networks underlying heterogeneous gray matter (GM) correlates of extraversion and to characterize the neurotransmitter receptor and transporter architecture associated with these networks.

Methods: A systematic literature search identified 13 voxel-based morphometry (VBM) studies reporting GM correlates of extraversion in healthy individuals (N = 1,478). Functional connectivity network mapping (FCNM) approach using normative resting-state functional MRI data from the Human Connectome Project (HCP, N = 1,093) mapped divergent GM correlates extraversion onto common networks. Robustness was assessed via replication using an independent Southwest University Adult Lifespan Dataset (SALD, N = 329) and sensitivity analyses varying seed radii. Spatial relationships between the identified brain networks and the distribution of major neurotransmitter receptors/transporters were subsequently characterized using the JuSpace toolbox.

Results: FCNM analysis revealed that reported GM correlates of extraversion converge onto specific functional networks. Spatial overlap analysis showed the highest association with the frontoparietal network (FPN) (37.32%) and the default mode network (DMN) (32.99%). Furthermore, JuSpace analysis indicated that these extraversion-linked networks exhibited significant positive spatial correlations with 5-hydroxytryptamine receptor 2A (5HT2a; p = 0.021, r = 0.215), cannabinoid receptor type-1 (CB1; p = 0.005, r = 0.392), and metabotropic glutamate receptor 5 (mGluR5; p = 0.01, r = 0.330), and negative correlations with the norepinephrine transporter (NAT; p = 0.018, r = -0.221) and serotonin transporter (SERT; p = 0.023, r = -0.201).

Conclusion: Despite regional heterogeneity in prior VBM studies, structural GM correlates of extraversion consistently map onto the DMN and FPN. This network-based approach reconciles previous inconsistencies and highlights the importance of these large-scale networks as a plausible functional substrate underlying structural variations associated with extraversion. These findings advance a systems-level understanding of the neural basis of this core personality dimension and suggest a distinct neurochemical architecture within these networks.

Introduction: The mechanisms by which conscious breathing influences brain-body signaling remain largely unexplored. Understanding how controlled breathing modulates neural and autonomic activity can offer insights into self-regulation and adaptive physiological control. This study investigates how conscious breathing affects cortical-autonomic communication by analyzing bidirectional interactions between EEG band power time series (BPts), heart rate variability (HRV), and breathing signals.

Methods: Data were collected from fifteen healthy subjects during three experimental conditions: a spontaneous breathing state (Rest) and two controlled breathing tasks (CBT 1 and CBT 2). EEG recordings were analyzed to compute BPts across the δ, θ, α, β, and γ frequency bands, while HRV and breathing signals were derived from ECG data. Cross-spectrum analysis and Granger causality tests were performed between HRV and BPts. To further investigate directional interactions, Granger-causal relationships were explored between components of the BPts extracted using empirical mode decomposition and the HRV and breathing signals.

Results: Bidirectional Granger-causal relationships were found between neural and autonomic systems, emphasizing the dynamic interaction between the brain and body. Specific BPts components mediated neural-autonomic communication, with one component consistently aligning with the frequency of conscious breathing (~0.05 Hz) during the CBTs. Cross-spectral peaks at this frequency and its harmonics highlight the role of respiratory entrainment in optimizing neuro-autonomic synchronization. Frequency-specific mechanisms observed in both fast and slow components reflect the complex regulation of autonomic functions through cortical modulation. The most prominent causal effects were observed in the γ band, suggesting its pivotal role in dynamic autonomic regulation, potentially acting as a communication pathway between the brain and body.

Discussion: Our results demonstrate that conscious breathing enhances bidirectional cortical-autonomic modulation through frequency-specific dynamic neural mechanisms. These findings support a closed-loop model of physiological regulation driven by neural-respiratory entrainment and suggest that respiration can serve as a top-down mechanism for autonomic control. By clarifying how conscious breathing shapes brain-body dynamics, this work lays the foundation for research on neural self-regulation and supports the development of non-pharmacological interventions for improving mental and physiological health.

This paper proposes that learning in animals occurs thru sleep and is fundamentally driven by dynamic information valuation processes. These take the form of either pain and pleasure sensations or the more nuanced emotions that evolved from them. Acting as value identifiers, these sensations and emotions enable animals, from the simplest to the most complex, to mark valuable experiences for both retention and later recall. In this way, the paper argues that learning itself is made possible. The remainder of the paper explores the cognitive, neurological and behavioral implications of this framework, including several novel, testable hypotheses derived from it.

Attention deficit hyperactivity disorder (ADHD) stands as one of the most prevalent neurodevelopmental disorders, affecting millions worldwide. While traditional pharmacological interventions have been the cornerstone of ADHD treatment, emerging novel methods such as transcranial Direct Current Stimulation (tDCS) and neurofeedback offer promising avenues for addressing the multifaceted challenges of ADHD management. This review paper critically synthesizes the current literature on tDCS and neurofeedback techniques in ADHD treatment, elucidating their mechanisms of action, efficacy, and potential as adjunct or alternative therapeutic modalities. By exploring these innovative approaches, this review aims to deepen our understanding of neurobiological underpinnings of ADHD and pave the way for more personalized and effective interventions, ultimately enhancing the quality of life for individuals grappling with ADHD symptoms.

Introduction: Caffeine is the most widely consumed psychoactive substance, and its stimulant properties are well documented, but few investigations have examined its acute effects on brain and cardiovascular responses during cognitively demanding tasks under ecologically valid conditions.

Method: This study used wearable biosensors and machine learning analysis to evaluate the effects of moderate caffeine (162 mg) on heart rate variability (HRV), entropy, pulse transit time (PTT), blood pressure, and EEG activity. Twelve healthy male participants (20-30 years) completed a within-subjects protocol with pre-caffeine and post-caffeine sessions. EEG was recorded from seven central electrodes (C3, Cz, C4, CP1, CP2, CP5, CP6) using the EMOTIV EPOC Flex system, and heart rate (HR) and blood pressure (BP) were continuously monitored via the Huawei Watch D. Data analysis included power spectral density (PSD) estimation, FOOOF decomposition, and unsupervised k-means clustering.

Results: Paired-sample t-tests assessed physiological and EEG changes. Although systolic and diastolic BP showed a non-significant upward trend, HR decreased significantly after caffeine intake (77 ± 5.3 bpm to 72 ± 2.5 bpm, p = 0.027). There was a significant increase in absolute alpha power suppression (from -5.1 ± 0.8 dB to -6.9 ± 0.9 dB, p = 0.04) and beta power enhancement (-4.7 ± 1.2 dB to -2.3 ± 1/1, p = 0.04). The surface data from FOOOF shows these are real oscillatory changes. Based on the changes in clustering prior and post-caffeine, a machine-learning change in the brain activity differentiated pre/post-caffeine states with unsupervised clustering. The study results show that moderate caffeine resulted in synchronized EEG and cardiovascular changes, indicating increased arousal and cortical activation that are detectable with wearable biosensors and classifiable with machine learning.

Conclusion: A fully integrated, non-invasive methodology based on a wearable device for real-time monitoring of cognitive states holds promise in the context of digital health, fatigue detection, and public health awareness efforts.

Measuring precise emotional tagging for taste information, with or without the use of words, is challenging. While affective taste valence and salience are core components of emotional experiences, traditional behavioral assays for taste preference, which often rely on cumulative consumption, lack the resolution to distinguish between different affective states, such as innate versus learned aversion, which are known to be mediated by distinct neural circuits. To overcome this limitation, we developed an open-source system for high-resolution microstructural analysis of licking behavior in freely moving mice. Our approach integrates traditional lick burst analysis with a proprietary software pipeline that utilizes interlick interval (ILI) distributions and principal component analysis (PCA) to create a multidimensional behavioral profile of the animal. Using this system, we characterized the licking patterns associated with innate appetitive, aversive, and neutral tastants. While conventional burst analysis failed to differentiate between two palatable stimuli (water and saccharin), our multidimensional approach revealed distinct and quantifiable behavioral signatures for each. Critically, this approach successfully dissociates innate and learned aversive taste valences, a distinction that cannot be achieved using standard metrics. By providing the designs for our custom-built setup and analysis software under an open-source license, this study offers a comprehensive and accessible methodology for examining hedonic responses in future studies. This powerful toolkit enhances our understanding of sensory valence processing and provides a robust platform for future investigations of the neurobiology of ingestive behavior.

Psilocybin, a compound found in Psilocybe mushrooms, is emerging as a promising treatment for neurodegenerative and psychiatric disorders, including major depressive disorder. Its potential therapeutic effects stem from promoting neuroprotection, neurogenesis, and neuroplasticity, key factors in brain health. Psilocybin could help combat mild neurodegeneration by increasing synaptic density and supporting neuronal growth. With low risk for addiction and adverse effects, it presents a safe option for long-term use, setting it apart from traditional treatments. Despite their relatively simpler neuronal networks, studies using animal models, such as Drosophila and fish, have provided essential insights on the efficacy and mechanism of action of psilocybin. These models provide foundational information that guides more focused investigations, facilitating high-throughput screening, enabling researchers to quickly explore the compound's effects on neural development, behavior, and underlying genetic pathways. While mammalian models are indispensable for comprehensive studies on psilocybin's pharmacokinetics and its nuanced interactions within the complex nervous systems, small non-mammalian models remain valuable for identifying promising targets and mechanisms at early research stages. Together, these animal systems offer a complementary approach to drive rapid hypothesis generation to refine our understanding of psilocybin as a candidate for not only halting but potentially reversing neurodegenerative processes. This integrative strategy highlights the transformative potential of psilocybin in addressing neurodegenerative disorders, leveraging both small and mammalian models to achieve translational research success.

This article describes a biological neural network model that explains how humans learn to understand large language models and their meanings. This kind of learning typically occurs when a student learns from a teacher about events that they experience together. Multiple types of self-organizing brain processes are involved, including content-addressable memory; conscious visual perception; joint attention; object learning, categorization, and cognition; conscious recognition; cognitive working memory; cognitive planning; neural-symbolic computing; emotion; cognitive-emotional interactions and reinforcement learning; volition; and goal-oriented actions. The article advances earlier results showing how small language models are learned that have perceptual and affective meanings. The current article explains how humans, and neural network models thereof, learn to consciously see and recognize an unlimited number of visual scenes. Then, bi-directional associative links can be learned and stably remembered between these scenes, the emotions that they evoke, and the descriptive language utterances associated with them. Adaptive resonance theory circuits control model learning and self-stabilizing memory. These human capabilities are not found in AI models such as ChatGPT. The current model is called ChatSOME, where SOME abbreviates Self-Organizing MEaning. The article summarizes neural network highlights since the 1950s and leading models, including adaptive resonance, deep learning, LLMs, and transformers.